ARC-Easy_Llama-3.2-1B-dygan5pn

This model is a fine-tuned version of meta-llama/Llama-3.2-1B on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 2.7461
• Model Preparation Time: 0.0057
• Mdl: 2258.2292
• Accumulated Loss: 1565.2852
• Correct Preds: 410.0
• Total Preds: 570.0
• Accuracy: 0.7193
• Correct Gen Preds: 387.0
• Gen Accuracy: 0.6789
• Correct Gen Preds 32: 99.0
• Correct Preds 32: 113.0
• Total Labels 32: 158.0
• Accuracy 32: 0.7152
• Gen Accuracy 32: 0.6266
• Correct Gen Preds 33: 113.0
• Correct Preds 33: 115.0
• Total Labels 33: 152.0
• Accuracy 33: 0.7566
• Gen Accuracy 33: 0.7434
• Correct Gen Preds 34: 96.0
• Correct Preds 34: 98.0
• Total Labels 34: 142.0
• Accuracy 34: 0.6901
• Gen Accuracy 34: 0.6761
• Correct Gen Preds 35: 79.0
• Correct Preds 35: 84.0
• Total Labels 35: 118.0
• Accuracy 35: 0.7119
• Gen Accuracy 35: 0.6695
• Correct Gen Preds 36: 0.0
• Correct Preds 36: 0.0
• Total Labels 36: 0.0
• Accuracy 36: 0.0
• Gen Accuracy 36: 0.0

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 64
• eval_batch_size: 112
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.01
• num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Mdl	Accumulated Loss	Correct Preds	Total Preds	Accuracy	Correct Gen Preds	Gen Accuracy	Correct Gen Preds 32	Correct Preds 32	Total Labels 32	Accuracy 32	Gen Accuracy 32	Correct Gen Preds 33	Correct Preds 33	Total Labels 33	Accuracy 33	Gen Accuracy 33	Correct Gen Preds 34	Correct Preds 34	Total Labels 34	Accuracy 34	Gen Accuracy 34	Correct Gen Preds 35	Correct Preds 35	Total Labels 35	Accuracy 35	Gen Accuracy 35	Correct Gen Preds 36	Correct Preds 36	Total Labels 36	Accuracy 36	Gen Accuracy 36
No log	0	0	1.5354	0.0057	1262.6022	875.1692	172.0	570.0	0.3018	170.0	0.2982	154.0	154.0	158.0	0.9747	0.9747	0.0	0.0	152.0	0.0	0.0	15.0	17.0	142.0	0.1197	0.1056	1.0	1.0	118.0	0.0085	0.0085	0.0	0.0	0.0	0.0	0.0
1.3867	1.0	6	1.0016	0.0057	823.6421	570.9052	379.0	570.0	0.6649	379.0	0.6649	111.0	111.0	158.0	0.7025	0.7025	120.0	120.0	152.0	0.7895	0.7895	85.0	85.0	142.0	0.5986	0.5986	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
1.1746	2.0	12	0.9085	0.0057	747.0919	517.8447	384.0	570.0	0.6737	384.0	0.6737	96.0	96.0	158.0	0.6076	0.6076	130.0	130.0	152.0	0.8553	0.8553	97.0	97.0	142.0	0.6831	0.6831	61.0	61.0	118.0	0.5169	0.5169	0.0	0.0	0.0	0.0	0.0
0.0054	3.0	18	1.5269	0.0057	1255.6062	870.3199	409.0	570.0	0.7175	409.0	0.7175	112.0	112.0	158.0	0.7089	0.7089	116.0	116.0	152.0	0.7632	0.7632	94.0	94.0	142.0	0.6620	0.6620	87.0	87.0	118.0	0.7373	0.7373	0.0	0.0	0.0	0.0	0.0
0.0	4.0	24	2.6585	0.0057	2186.1764	1515.3420	406.0	570.0	0.7123	405.0	0.7105	93.0	94.0	158.0	0.5949	0.5886	126.0	126.0	152.0	0.8289	0.8289	104.0	104.0	142.0	0.7324	0.7324	82.0	82.0	118.0	0.6949	0.6949	0.0	0.0	0.0	0.0	0.0
0.0	5.0	30	2.9638	0.0057	2437.2343	1689.3621	396.0	570.0	0.6947	388.0	0.6807	123.0	129.0	158.0	0.8165	0.7785	107.0	107.0	152.0	0.7039	0.7039	88.0	89.0	142.0	0.6268	0.6197	70.0	71.0	118.0	0.6017	0.5932	0.0	0.0	0.0	0.0	0.0
0.0	6.0	36	2.7461	0.0057	2258.2292	1565.2852	410.0	570.0	0.7193	387.0	0.6789	99.0	113.0	158.0	0.7152	0.6266	113.0	115.0	152.0	0.7566	0.7434	96.0	98.0	142.0	0.6901	0.6761	79.0	84.0	118.0	0.7119	0.6695	0.0	0.0	0.0	0.0	0.0
0.6357	7.0	42	2.8084	0.0057	2309.4303	1600.7751	403.0	570.0	0.7070	267.0	0.4684	46.0	108.0	158.0	0.6835	0.2911	87.0	115.0	152.0	0.7566	0.5724	74.0	98.0	142.0	0.6901	0.5211	60.0	82.0	118.0	0.6949	0.5085	0.0	0.0	0.0	0.0	0.0
1.9179	8.0	48	2.8006	0.0057	2303.0086	1596.3239	402.0	570.0	0.7053	60.0	0.1053	5.0	121.0	158.0	0.7658	0.0316	20.0	106.0	152.0	0.6974	0.1316	15.0	100.0	142.0	0.7042	0.1056	20.0	75.0	118.0	0.6356	0.1695	0.0	0.0	0.0	0.0	0.0
0.0001	9.0	54	3.0054	0.0057	2471.4361	1713.0690	405.0	570.0	0.7105	153.0	0.2684	18.0	121.0	158.0	0.7658	0.1139	45.0	106.0	152.0	0.6974	0.2961	49.0	104.0	142.0	0.7324	0.3451	41.0	74.0	118.0	0.6271	0.3475	0.0	0.0	0.0	0.0	0.0
0.0	10.0	60	3.1819	0.0057	2616.5819	1813.6764	404.0	570.0	0.7088	302.0	0.5298	70.0	120.0	158.0	0.7595	0.4430	84.0	105.0	152.0	0.6908	0.5526	87.0	102.0	142.0	0.7183	0.6127	61.0	77.0	118.0	0.6525	0.5169	0.0	0.0	0.0	0.0	0.0
0.0	11.0	66	3.2572	0.0057	2678.5170	1856.6065	402.0	570.0	0.7053	349.0	0.6123	93.0	121.0	158.0	0.7658	0.5886	95.0	105.0	152.0	0.6908	0.625	95.0	101.0	142.0	0.7113	0.6690	66.0	75.0	118.0	0.6356	0.5593	0.0	0.0	0.0	0.0	0.0
0.0	12.0	72	3.2819	0.0057	2698.8538	1870.7029	405.0	570.0	0.7105	365.0	0.6404	99.0	121.0	158.0	0.7658	0.6266	99.0	105.0	152.0	0.6908	0.6513	96.0	101.0	142.0	0.7113	0.6761	71.0	78.0	118.0	0.6610	0.6017	0.0	0.0	0.0	0.0	0.0
0.0	13.0	78	3.3385	0.0057	2745.3597	1902.9383	404.0	570.0	0.7088	373.0	0.6544	103.0	121.0	158.0	0.7658	0.6519	101.0	105.0	152.0	0.6908	0.6645	96.0	100.0	142.0	0.7042	0.6761	73.0	78.0	118.0	0.6610	0.6186	0.0	0.0	0.0	0.0	0.0
0.0	14.0	84	3.3290	0.0057	2737.5652	1897.5356	403.0	570.0	0.7070	376.0	0.6596	106.0	121.0	158.0	0.7658	0.6709	102.0	105.0	152.0	0.6908	0.6711	96.0	100.0	142.0	0.7042	0.6761	72.0	77.0	118.0	0.6525	0.6102	0.0	0.0	0.0	0.0	0.0
0.0	15.0	90	3.3171	0.0057	2727.7934	1890.7623	403.0	570.0	0.7070	375.0	0.6579	105.0	121.0	158.0	0.7658	0.6646	101.0	105.0	152.0	0.6908	0.6645	96.0	100.0	142.0	0.7042	0.6761	73.0	77.0	118.0	0.6525	0.6186	0.0	0.0	0.0	0.0	0.0
0.0	16.0	96	3.3260	0.0057	2735.1033	1895.8291	402.0	570.0	0.7053	374.0	0.6561	105.0	121.0	158.0	0.7658	0.6646	101.0	105.0	152.0	0.6908	0.6645	96.0	100.0	142.0	0.7042	0.6761	72.0	76.0	118.0	0.6441	0.6102	0.0	0.0	0.0	0.0	0.0
0.0	17.0	102	3.3211	0.0057	2731.0335	1893.0082	406.0	570.0	0.7123	380.0	0.6667	108.0	122.0	158.0	0.7722	0.6835	102.0	106.0	152.0	0.6974	0.6711	96.0	100.0	142.0	0.7042	0.6761	74.0	78.0	118.0	0.6610	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	18.0	108	3.3253	0.0057	2734.5364	1895.4362	404.0	570.0	0.7088	377.0	0.6614	105.0	121.0	158.0	0.7658	0.6646	102.0	106.0	152.0	0.6974	0.6711	96.0	100.0	142.0	0.7042	0.6761	74.0	77.0	118.0	0.6525	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	19.0	114	3.3377	0.0057	2744.6939	1902.4768	402.0	570.0	0.7053	378.0	0.6632	108.0	121.0	158.0	0.7658	0.6835	102.0	105.0	152.0	0.6908	0.6711	95.0	99.0	142.0	0.6972	0.6690	73.0	77.0	118.0	0.6525	0.6186	0.0	0.0	0.0	0.0	0.0
0.0	20.0	120	3.3364	0.0057	2743.6099	1901.7254	405.0	570.0	0.7105	382.0	0.6702	108.0	121.0	158.0	0.7658	0.6835	103.0	105.0	152.0	0.6908	0.6776	97.0	101.0	142.0	0.7113	0.6831	74.0	78.0	118.0	0.6610	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	21.0	126	3.3373	0.0057	2744.3570	1902.2433	405.0	570.0	0.7105	379.0	0.6649	107.0	121.0	158.0	0.7658	0.6772	102.0	106.0	152.0	0.6974	0.6711	96.0	100.0	142.0	0.7042	0.6761	74.0	78.0	118.0	0.6610	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	22.0	132	3.3504	0.0057	2755.1293	1909.7101	404.0	570.0	0.7088	381.0	0.6684	109.0	121.0	158.0	0.7658	0.6899	103.0	106.0	152.0	0.6974	0.6776	96.0	100.0	142.0	0.7042	0.6761	73.0	77.0	118.0	0.6525	0.6186	0.0	0.0	0.0	0.0	0.0
0.0	23.0	138	3.3447	0.0057	2750.4965	1906.4989	407.0	570.0	0.7140	380.0	0.6667	108.0	122.0	158.0	0.7722	0.6835	103.0	106.0	152.0	0.6974	0.6776	96.0	101.0	142.0	0.7113	0.6761	73.0	78.0	118.0	0.6610	0.6186	0.0	0.0	0.0	0.0	0.0
0.0	24.0	144	3.3547	0.0057	2758.6616	1912.1585	404.0	570.0	0.7088	382.0	0.6702	109.0	121.0	158.0	0.7658	0.6899	103.0	105.0	152.0	0.6908	0.6776	96.0	100.0	142.0	0.7042	0.6761	74.0	78.0	118.0	0.6610	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	25.0	150	3.3695	0.0057	2770.8605	1920.6142	405.0	570.0	0.7105	381.0	0.6684	109.0	121.0	158.0	0.7658	0.6899	102.0	105.0	152.0	0.6908	0.6711	96.0	101.0	142.0	0.7113	0.6761	74.0	78.0	118.0	0.6610	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	26.0	156	3.3721	0.0057	2772.9899	1922.0901	403.0	570.0	0.7070	383.0	0.6719	111.0	121.0	158.0	0.7658	0.7025	102.0	104.0	152.0	0.6842	0.6711	96.0	101.0	142.0	0.7113	0.6761	74.0	77.0	118.0	0.6525	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	27.0	162	3.3721	0.0057	2772.9858	1922.0873	404.0	570.0	0.7088	382.0	0.6702	111.0	121.0	158.0	0.7658	0.7025	102.0	105.0	152.0	0.6908	0.6711	96.0	101.0	142.0	0.7113	0.6761	73.0	77.0	118.0	0.6525	0.6186	0.0	0.0	0.0	0.0	0.0
0.0	28.0	168	3.3810	0.0057	2780.3503	1927.1920	401.0	570.0	0.7035	380.0	0.6667	110.0	121.0	158.0	0.7658	0.6962	103.0	105.0	152.0	0.6908	0.6776	95.0	99.0	142.0	0.6972	0.6690	72.0	76.0	118.0	0.6441	0.6102	0.0	0.0	0.0	0.0	0.0
0.0	29.0	174	3.3540	0.0057	2758.0755	1911.7523	404.0	570.0	0.7088	385.0	0.6754	112.0	121.0	158.0	0.7658	0.7089	103.0	105.0	152.0	0.6908	0.6776	96.0	100.0	142.0	0.7042	0.6761	74.0	78.0	118.0	0.6610	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	30.0	180	3.3641	0.0057	2766.4616	1917.5651	402.0	570.0	0.7053	381.0	0.6684	111.0	121.0	158.0	0.7658	0.7025	102.0	105.0	152.0	0.6908	0.6711	96.0	100.0	142.0	0.7042	0.6761	72.0	76.0	118.0	0.6441	0.6102	0.0	0.0	0.0	0.0	0.0
0.0	31.0	186	3.3698	0.0057	2771.1427	1920.8097	404.0	570.0	0.7088	381.0	0.6684	110.0	121.0	158.0	0.7658	0.6962	102.0	105.0	152.0	0.6908	0.6711	96.0	101.0	142.0	0.7113	0.6761	73.0	77.0	118.0	0.6525	0.6186	0.0	0.0	0.0	0.0	0.0
0.0	32.0	192	3.3674	0.0057	2769.1561	1919.4328	402.0	570.0	0.7053	382.0	0.6702	110.0	121.0	158.0	0.7658	0.6962	102.0	104.0	152.0	0.6842	0.6711	96.0	100.0	142.0	0.7042	0.6761	74.0	77.0	118.0	0.6525	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	33.0	198	3.3907	0.0057	2788.3243	1932.7191	403.0	570.0	0.7070	385.0	0.6754	111.0	121.0	158.0	0.7658	0.7025	103.0	105.0	152.0	0.6908	0.6776	96.0	100.0	142.0	0.7042	0.6761	75.0	77.0	118.0	0.6525	0.6356	0.0	0.0	0.0	0.0	0.0
0.0	34.0	204	3.3846	0.0057	2783.2820	1929.2241	404.0	570.0	0.7088	383.0	0.6719	111.0	121.0	158.0	0.7658	0.7025	102.0	105.0	152.0	0.6908	0.6711	95.0	100.0	142.0	0.7042	0.6690	75.0	78.0	118.0	0.6610	0.6356	0.0	0.0	0.0	0.0	0.0
0.0	35.0	210	3.3754	0.0057	2775.7349	1923.9928	403.0	570.0	0.7070	386.0	0.6772	113.0	121.0	158.0	0.7658	0.7152	102.0	105.0	152.0	0.6908	0.6711	97.0	100.0	142.0	0.7042	0.6831	74.0	77.0	118.0	0.6525	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	36.0	216	3.3802	0.0057	2779.6781	1926.7260	406.0	570.0	0.7123	387.0	0.6789	112.0	122.0	158.0	0.7722	0.7089	103.0	105.0	152.0	0.6908	0.6776	96.0	101.0	142.0	0.7113	0.6761	76.0	78.0	118.0	0.6610	0.6441	0.0	0.0	0.0	0.0	0.0

Framework versions

• Transformers 4.51.3
• Pytorch 2.6.0+cu124
• Datasets 3.5.0
• Tokenizers 0.21.1